1. Introduction

In relation to policy, "the environment" is particularly challenging. It
includes masses of detail concerning many particular issues, which require
separate analysis and management. At the same time, there are broad strategic
issues, which should guide regulatory work, such as those connected with
"sustainability". Nothing can be managed in a convenient isolation; issues
are mutually implicated; problems extend across many scale levels of space
and time; and uncertainties and value-loadings of all sorts and all degrees
of severity affect data and theories alike.

This situation is a new one for policy makers. In one sense the environment
is in the domain of Science: the phenomena of concern are located in the
world of nature. Yet the tasks are totally different from those traditionally
conceived for Western science. For that, it was a matter of conquest and
control of Nature; now we must manage, accommodate and adjust. We know
that we are no longer, and never really were, the "masters and possessors
of Nature" that Descartes imagined for our role in the world (Descartes
1638).

To engage in these new tasks we need new intellectual tools. A picture
of reality designed for controlled experimentation and abstract theory
building, can be very effective with complex phenomena reduced to their
simple, atomic elements. But it is not best suited for the tasks of environmental
policy today. The scientific mind-set fosters expectations of regularity,
simplicity and certainty in the phenomena and in our interventions. But
these can inhibit the growth of our understanding of the problems and of
appropriate methods to their solution. Here we shall introduce and articulate
several concepts, which can provide elements of a framework to understand
environmental issues. They are all new, and still evolving. There is no
orthodoxy concerning their content or the conditions of their application

The leading concept is "complexity". This relates to the structure and
properties of the phenomena and the issues for environmental policy. Systems
that are complex are not merely complicated; by their nature they involve
deep uncertainties and a plurality of legitimate perspectives. Hence the
methodologies of traditional laboratory-based science are of restricted
effectiveness in this new context.

The most general methodology for managing complex science-related issues
is "Post-Normal Science" (Funtowicz and Ravetz 1992, 1993, Futures 1999).
This focuses on aspects of problem solving that tend to be neglected in
traditional accounts of scientific practice: uncertainty and value loading.
It provides a coherent explanation of the need for greater participation
in science-policy processes, based on the new tasks of quality assurance
in these problem-areas.

2. Complexity

Anyone trying to comprehend the problems of "the environment" might well
be bewildered by their number, variety and complication. There is a natural
temptation to try to reduce them to simpler, more manageable elements,
as with mathematical models and computer simulations. This, after all,
has been the successful programme of Western science and technology up
to now. But environmental problems have features which prevent reductionist
approaches from having any, but the most limited useful effect. These are
what we mean when we use the term "complexity".

Complexity is a property of certain sorts of systems; it distinguishes
them from those which are simple, or merely complicated. Simple systems
can be captured (in theory or in practice) by a deterministic, linear causal
analysis. Such are the classic scientific explanations, notably those of
high-prestige fields like mathematical physics. Sometimes such a system
requires more variables for its explanation or control than can be neatly
managed in its theory. Then the task is accomplished by other methods;
and the system is "complicated". The distinction between science and engineering,
the latter occurring when more than a half-dozen variables are in play,
is a good example of the distinction between simple and complicated systems.

With true complexity, we are dealing with phenomena of a different sort.
There are many definitions of complexity, all overlapping, deriving from
the various areas of scientific practice with, for example, ecological
systems, organisms, social institutions, or the "artificial" simulations
of any of them. Here we adopt a more general approach to the concept. First,
we think of a "system", a collection of elements and subsystems, defined
by their relations within some sort of hierarchy or hierarchies. The hierarchy
may be one of inclusion and scale, as in an ecosystem with (say) a pond,
its stream, the watershed, and the region, at ascending levels. Or it may
be a hierarchy of function, as in an organism and its separate organs.
A species and its individual members form a system with hierarchies of
both inclusion and function. Environmental systems may also include human
and institutional sub-systems, which are themselves systems. These latter
are a very special sort of system, which we call "reflexive". In those,
the elements have purposes of their own, which they may attempt to achieve
independently of, or even in opposition to, their assigned functions in
the hierarchy (Funtowicz and Ravetz 1997b).

First, any "system" is itself an intellectual construct, that some humans
have imposed on a set of phenomena and their explanations. Sometimes it
is convenient to leave the observer out of the system; but in the cases
of systems with human and institutional components, this is counterproductive.
For environmental systems, then, the observer and analyst are there, as
embedded in their own systems, variously social, geographical and cognitive.
For policy purposes, a very basic property of observed and analysed complex
systems might be called "feeling the elephant", after the Indian fable
of the five blind men trying to guess the object they were touching by
feeling a part of an elephant. Each conceived the object after his own
partial imaging process (the leg indicated a tree, the side a wall, the
trunk a snake, etc); it was left to an outsider observer to visualise the
whole elephant. This parable reminds us that every observer and analyst
of a complex system operates with certain criteria of selection of phenomena,
at a certain scale-level, and with certain built-in values and commitments.
The result of their separate observations and analyses are not at all "purely
subjective" or arbitrary; but none of them singly can encompass the whole
system. Looking at the process as a whole, we may ask whether an awareness
of their own limitations is built into their personal systematic understanding,
or whether it is excluded. In the absence of such awareness, we have old-fashioned
technical expertise; when analysis is enriched by its presence, we have
Post-Normal Science.

We can express the point in a somewhat more systematic fashion, in terms
of two key properties of complex systems. One is the presence of significant
and irreducible uncertainties of various sorts in any analysis; and the
other is a multiplicity of legitimate perspectives on any problem. For
the uncertainty, we have a sort of "Heisenberg effect", where the acts
of observation and analysis become part of the activity of the system under
study, and so influence it in various ways. This is well known in reflexive
social systems, through the phenomena of "moral hazard", self-fulfilling
prophecies and mass panic.

But there is another cause of uncertainty, more characteristic of complex
systems. This derives from the fact that any analysis (and indeed any observation)
must deal with an artificial, usually truncated system. The concepts in
whose terms existing data is organised will only accidentally coincide
with the boundaries and structures that are relevant to a given policy
issue. Thus, social and environmental statistics are usually available
(if at all) in aggregations created by governments with other problems
in mind; they need interpreting or massaging to make them relevant to the
problem at hand. Along with their obvious, technical uncertainties resulting
from the operations of data collection and aggregation, the data will have
deeper, structural uncertainties, not amenable to quantitative analysis,
which may actually be decisive for the quality of the information being
presented.

A similar analysis yields the conclusion that there is no unique, privileged
perspective on the system. The criteria for selection of data, truncation
of models, and formation of theoretical constructs are value-laden, and
the values are those embodied in the societal or institutional system in
which the science is being done. This is not a proclamation of "relativism"
or anarchy. Rather, it is a reminder that the decision process on environmental
policies must include dialogue among those who have an interest in the
issue and a commitment to its solution. It also suggests that the process
towards a decision may be as important as the details of the decision that
is finally achieved.

For an example of this plurality of perspectives, we may imagine a group
of people gazing at a hillside. One of them "sees" a particular sort of
forest, another an archaeological site; another a potential suburb, yet
another sees a planning problem. Each uses their training to evaluate what
they see, in relation to their tasks. Their perceptions are conditioned
by a variety of structures, cognitive and institutional, with both explicit
and tacit elements. In a policy process, their separate visions may well
come into conflict, and some stakeholders may even deny the legitimacy
of the commitments and the validity of the perceptions of others. Each
perceives his or her own elephant, as it were. The task of the facilitator
is to see those partial systems from a broader perspective, and to find
or create some overlap among them all, so that there can be agreement or
at least acquiescence in a policy. For those who have this integrating
task, it helps to understand that this diversity and possible conflict
is not an unfortunate accident that could be eliminated by better natural
or social science. It is inherent to the character of the complex system
that is realised in that particular hillside.

These two key properties of complex systems, radical uncertainty and
plurality of legitimate perspectives, help to define the programme. They
show why environmental policy can not be shaped around the idealised linear
path of the gathering and then the application of scientific knowledge.
Rather, the formation of policy is itself embedded as a subsystem in the
total complex system of which its environmental problem is another element.

3. Post-Normal Science as a bridge between complex systems and environmental
policy

The idea of a science being somehow "post-normal" conveys an air of paradox
and perhaps mystery. By "normality" we mean two things. One is the picture
of research science as "normally" consisting of puzzle solving within an
unquestioned and unquestionable "paradigm", in the theory of T.S. Kuhn
(Kuhn 1962). Another is the assumption that the policy environment is still
"normal", in that such routine puzzle solving by experts provides an adequate
knowledge base for policy decisions. Of course researchers and experts
must do routine work on small-scale problems; the question is how the framework
is set, by whom, and with whose awareness of the process. In "normality",
either science or policy, the process is managed largely implicitly, and
is accepted unwittingly by all who wish to join in. The great lesson of
recent years is that that assumption no longer holds. We may call it a
"post-modern" "rejection of grand narratives", or a green, NIMBY (Not In
My Back Yard) politics. Whatever its causes, we can no longer assume the
presence of this sort of "normality" of the policy process, particularly
in relation to the environment.

The insight leading to Post-Normal Science is that in the sorts of issue-driven
science relating to environmental debates, typically facts are uncertain,
values in dispute, stakes high, and decisions urgent. Some might say that
such problems should not be called "science"; but the answer could be that
such problems are everywhere, and when science is (as it must be) applied
to them, the conditions are anything but "normal". For the previous distinction
between "hard", objective scientific facts and "soft", subjective value-judgements
is now inverted. All too often, we must make hard policy decisions where
our only scientific inputs are irremediably soft.

The difference between old and new conditions can be shown by the present
difficulties of the classical economics approach to environmental policy.
Traditionally, economics attempted to show how social goals could be best
achieved by means of mechanisms operating automatically, in an essentially
simple system. The "hidden hand" metaphor of Adam Smith conveyed the idea
that conscious interference in the workings of the economic system would
do no good and much harm; and this view has persisted from then to now.
But for the achievement of sustainability, automatic mechanisms are clearly
insufficient. Even when pricing rather than control is used for implementation
of economic policies, the prices must be set, consciously, by some agency;
and this is then a highly visible controlling hand. When externalities
are uncertain and irreversible, then no one can set "ecologically correct
prices" practised in actual markets or in fictitious markets (through contingent
valuation or other economic techniques). There might at best be "ecologically
corrected prices", set by a decision-making system. The hypotheses, theories,
visions and prejudices of the policy-setting agents are then in play, sometimes
quite publicly so. And the public also sees contrasting and conflicting
visions among those in the policy arena, all of which are plausible and
none of which admits of refutation by any other. This is a social system,
which, in the terms discussed above, is truly complex, indeed reflexively
complex.

In such contexts of complexity, there is a new role for natural science.
The facts that are taught from textbooks in institutions are still necessary,
but are no longer sufficient. For these relate to a standardised version
of the natural world, frequently to the artificially pure and stable conditions
of a laboratory experiment. The world as we interact with it in working
for sustainability, is quite different. Those who have become accredited
experts through a course of academic study, have much valuable knowledge
in relation to these practical problems. But they may also need to recover
from the mindset they might absorb unconsciously from their instruction.
Contrary to the impression conveyed by textbooks, most problems in practice
have more than one plausible answer; and many have no answer at all.

Further, in the artificial world studied in academic courses, it is
strictly inconceivable that problems could be tackled and solved except
by deploying the accredited expertise. Systems of management of environmental
problems that do not involve science, and which cannot be immediately explained
on scientific principles, are commonly dismissed as the products of blind
tradition or chance. And when persons with no formal qualifications attempt
to participate in the processes of innovation, evaluation or decision,
their efforts are viewed with scorn or suspicion. Such attitudes do not
arise from malevolence; they are inevitable products of a scientific training
which presupposes and then indoctrinates the assumption that all problems
are simple and scientific, to be solved on the analogy of the textbook.

It is when the textbook analogy fails, that science in the policy context
must become post-normal. When facts are uncertain, values in dispute, stakes
high, and decisions urgent the traditional guiding principle of research
science, the goal of achievement of truth or at least of factual knowledge,
must be substantially modified. In post-normal conditions, such products
may be a luxury, indeed an irrelevance. Here, the guiding principle is
a more robust one, that of quality.

It could well be argued that quality has always been the effective principle
in practical research science, but it was largely ignored by the dominant
philosophy and ideology of science. For post-normal science, quality becomes
crucial, and quality refers to process at least as much as to product.
It is increasingly realised in policy circles that in complex environment
issues, lacking neat solutions and requiring support from all stakeholders,
the quality of the decision-making process is absolutely critical for the
achievement of an effective product in the decision. This new understanding
applies to the scientific aspect of decision-making as much as to any other.

Figure 1

Post-Normal Science can be located in relation to the more traditional
complementary strategies, by means of a diagram (see Figure 1). On it,
we see two axes, "systems uncertainties" and "decision stakes". When both
are small, we are in the realm of "normal", safe science, where expertise
is fully effective. When either is medium, then the application of routine
techniques is not enough; skill, judgement, sometimes even courage are
required. We call this "professional consultancy", with the examples of
the surgeon or the senior engineer in mind. Our modern society has depended
on armies of "applied scientists" pushing forward the frontiers of knowledge
and technique, with the professionals performing an aristocratic role,
either as innovators or as guardians.

Of course there have always been problems that science could not solve;
indeed, the great achievement of our civilisation has been to tame nature
in so many ways, so that for unprecedented numbers of people, life is more
safe, convenient and comfortable than could ever have been imagined in
earlier times. But now we are finding that the conquest of nature is not
complete. As we confront nature in its reactive state, we find extreme
uncertainties in our understanding of its complex systems, uncertainties
that will not be resolved by mere growth in our data-bases or computing
power. And since we are all involved with managing the natural world to
our personal and sectional advantage, any policy for change is bound to
affect our interests. Hence in any problem-solving strategy, the decision-stakes
of the various stakeholders must also be reckoned with.

This is why the diagram has two dimensions; this is an innovation for
descriptions of "science", which had traditionally been assumed to be "value-free".
But in any real problem of environmental management, the two dimensions
are inseparable. When conclusions are not completely determined by the
scientific facts, inferences will (naturally and legitimately) be conditioned
by the values held by the agent. This is a necessary part of ordinary research
practice; all statistical tests have values built in through the choice
of numerical "confidence limits", and the management of "outlier" data
calls for judgements that can sometimes approach the post-normal in their
complexity. If the stakes are very high (as when an institution is seriously
threatened by a policy) then a defensive policy will involve challenging
every step of a scientific argument, even if the systems uncertainties
are actually small. Such tactics become wrong only when they are conducted
covertly, as by scientists who present themselves as impartial judges when
they are actually committed advocates. There are now many initiatives,
increasing in number and significance all the time, for involving wider
circles of people in decision-making and implementation on environmental
issues.

The contribution of all the stakeholders in cases of Post-Normal Science
is not merely a matter of broader democratic participation. For these new
problems are in many ways different from those of research science, professional
practice, or industrial development. Each of those has its means for quality
assurance of the products of the work, be they peer review, professional
associations, or the market. For these new problems, quality depends on
open dialogue between all those affected. This we call an "extended peer
community", consisting not merely of persons with some form or other of
institutional accreditation, but rather of all those with a desire to participate
in the resolution of the issue. Seen out of context, such a proposal might
seem to involve a dilution of the authority of science, and its dragging
into the arena of politics. But we are here not talking about the traditional
areas of research and industrial development; but about those where issues
of quality are crucial, and traditional mechanisms of quality assurance
are patently inadequate. Since this context of science is one involving
policy, we might see this extension of peer communities as analogous to
earlier extensions of franchise in other fields, as allowing workers to
form trade unions and women to vote. In all such cases, there were prophecies
of doom, which were not realised.

For the formation of environmental policy under conditions of complexity,
it is hard to imagine any viable alternative to extended peer communities.
They are already being created, in increasing numbers, either when the
authorities cannot see a way forward, or know that without a broad base
of consensus, no policies can succeed. They are called "citizens' juries",
"focus groups", or "consensus conferences", or any one of a great variety
of names; and their forms and powers are correspondingly varied. But they
all have one important element in common: they assess the quality of policy
proposals, including a scientific element, on the basis of whatever science
they can master during the preparation period. And their verdicts all have
some degree of moral force and hence political influence.

Along with this regulatory, evaluative function of extended peer communities,
another, more intimately involved in the policy process, is springing up.
Particularly at the local level, the discovery is being made, again and
again, that people not only care about their environment but also can become
ingenious and creative in finding practical, partly technological, ways
towards its improvement. Here the quality is not merely in the verification,
but also in the creation; as local people can imagine solutions and reformulate
problems in ways that the accredited experts, with the best will in the
world, do not find "normal" within their professional paradigms.

None can claim that the restoration of quality through extended peer
communities will occur easily, and without its own sorts of errors. But
in the processes of extension of peer communities through the approach
of Post-Normal Science, we can see a way forward, for science as much as
for the complex problems of the environment.

A sort of manual for Post-Normal Science practice has recently been
produced by the UK Royal Commission on Environmental Pollution. In its
21st Report, on Setting Environmental Standards, makes a number of observations
and recommendations reflecting this new understanding. Thus, on uncertainty,
we have:

9.49: No satisfactory way has been devised of measuring risk to the
natural environment, even in principle, let alone defining what scale of
risk should be regarded as tolerable;

on values:

9.74: When environmental standards are set or other judgements made
about environmental issues, decisions must be informed by an understanding
of peoples’ values. …;

and on extended peer communities:

9.74 (continued): Traditional forms of consultation, while they have
provided useful insights, are not an adequate method of articulating values;

and on a plurality of legitimate perspectives:

9.76: A more rigorous and wide-ranging exploration of people’s values
requires discussion and debate to allow a range of viewpoints and perspectives
to be considered, and individual values developed.

4. Conclusion

The inadequacies of the traditional "normal science" approach have been
revealed with dramatic clarity in the episode of "mad cow" disease. For
years the accredited researchers and advisors assured the British government
that the risk of transfer of the infective agent to humans was not significant.
They did not stress the decision-stakes involved in the official policy,
in which public alarm and government expense were the main perceived dangers.
Then infection of humans was confirmed, and for a brief period the government
admitted that an epidemic of degenerative disease was a "non-quantifiable
risk". The situation went out of control, and the revulsion of consumers
threatened not only British beef, but also perhaps the entire European
meat industry. At this stage there had to be a "hard" decision to be taken,
on the number of cattle to be destroyed, whose basis was a very "soft"
estimate of how many cattle deaths would be needed to reassure the meat-eating
public. At the same time, independent critics who had been dealt with quite
harshly in the past were admitted into the dialogue. Without in any way
desiring such an outcome, the British Ministry of Agriculture, Forests
and Fisheries had created a situation of extreme systems uncertainty, vast
decision stakes, and a legitimated extended peer community.

The Post-Normal Science approach needs not be interpreted as an attack
on the accredited experts, but rather as assistance. The world of "normal
science" in which they were trained has its place in any scientific study
of the environment, but it needs to be supplemented by awareness of the
"post-normal" nature of the problems we now confront. The management of
complex natural systems as if they were simple scientific exercises has
brought us to our present mixture of triumph and peril. We are now witnessing
the emergence of a new approach to problem-solving strategies in which
the role of science, still essential, is now appreciated in its full context
of the uncertainties of natural systems and the relevance of human values.